






'''simple docstring'''





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCamelCase						:						int  =					16
_UpperCamelCase						:						List[str]  =					32
def __UpperCAmelCase   (					A					:			Accelerator			,       A					:			int = 1_6		)    ->   int:
			UpperCAmelCase_      :       str          =       AutoTokenizer.from_pretrained('''bert-base-cased'''		)
			UpperCAmelCase_      :       int          =       load_dataset('''glue'''			,       '''mrpc'''		)

			def tokenize_function(A					:			Tuple		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_      :       List[str]          =       tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=A			,       max_length=A		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			# starting with the main process first:
			with accelerator.main_process_first():
						UpperCAmelCase_      :       Tuple          =       datasets.map(
						    A			,       batched=A			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       )

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_      :       str          =       tokenized_datasets.rename_column('''label'''			,       '''labels'''		)

			def collate_fn(A					:			Any		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						UpperCAmelCase_      :       Any          =       1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									UpperCAmelCase_      :       Optional[Any]          =       1_6
						elif accelerator.mixed_precision != "no":
									UpperCAmelCase_      :       Union[str, Any]          =       8
						else:
									UpperCAmelCase_      :       Tuple          =       None

						return tokenizer.pad(
						    A			,       padding='''longest'''			,       max_length=A			,       pad_to_multiple_of=A			,       return_tensors='''pt'''			,       )

			# Instantiate dataloaders.
			UpperCAmelCase_      :       Tuple          =       DataLoader(
			    tokenized_datasets['''train''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)
			UpperCAmelCase_      :       Optional[int]          =       DataLoader(
			    tokenized_datasets['''validation''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)

			return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						_UpperCamelCase						:						str  =					mocked_dataloaders  # noqa: F811
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			List[Any]		)    ->   Union[str, Any]:
			# For testing only
			if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,       A		) == "1":
						UpperCAmelCase_      :       str          =       2
			# New Code #
			UpperCAmelCase_      :       int          =       int(args.gradient_accumulation_steps		)
			# Initialize accelerator
			UpperCAmelCase_      :       Tuple          =       Accelerator(
			    cpu=args.cpu			,       mixed_precision=args.mixed_precision			,       gradient_accumulation_steps=A		)
			if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
						raise NotImplementedError(
						    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''		)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_      :       int          =       config['''lr''']
			UpperCAmelCase_      :       Union[str, Any]          =       int(config['''num_epochs''']		)
			UpperCAmelCase_      :       Dict          =       int(config['''seed''']		)
			UpperCAmelCase_      :       int          =       int(config['''batch_size''']		)

			UpperCAmelCase_      :       Optional[Any]          =       evaluate.load('''glue'''			,       '''mrpc'''		)

			set_seed(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       get_dataloaders(A			,       A		)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_      :       Any          =       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,       return_dict=A		)

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			UpperCAmelCase_      :       Optional[int]          =       model.to(accelerator.device		)

			# Instantiate optimizer
			UpperCAmelCase_      :       Optional[Any]          =       AdamW(params=model.parameters()			,       lr=A		)

			# Instantiate scheduler
			UpperCAmelCase_      :       str          =       get_linear_schedule_with_warmup(
			    optimizer=A			,       num_warmup_steps=1_0_0			,       num_training_steps=(len(A		) * num_epochs)			,       )

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       accelerator.prepare(
			    A			,       A			,       A			,       A			,       A		)

			# Now we train the model
			for epoch in range(A		):
						model.train()
						for step, batch in enumerate(A		):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device		)
									# New code #
									# We use the new `accumulate` context manager to perform gradient accumulation
									# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
									with accelerator.accumulate(A		):
												UpperCAmelCase_      :       Any          =       model(**A		)
												UpperCAmelCase_      :       str          =       output.loss
												accelerator.backward(A		)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(A		):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device		)
									with torch.no_grad():
												UpperCAmelCase_      :       Tuple          =       model(**A		)
									UpperCAmelCase_      :       Union[str, Any]          =       outputs.logits.argmax(dim=-1		)
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
									metric.add_batch(
									    predictions=A			,       references=A			,       )

						UpperCAmelCase_      :       Dict          =       metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"epoch {epoch}:"			,       A		)
def __UpperCAmelCase   (					)    ->   int:
			UpperCAmelCase_      :       List[str]          =       argparse.ArgumentParser(description='''Simple example of training script.'''		)
			parser.add_argument(
			    '''--mixed_precision'''			,       type=A			,       default=A			,       choices=['''no''', '''fp16''', '''bf16''', '''fp8''']			,       help='''Whether to use mixed precision. Choose'''
			    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
			    '''and an Nvidia Ampere GPU.'''			,       )
			# New Code #
			parser.add_argument(
			    '''--gradient_accumulation_steps'''			,       type=A			,       default=1			,       help='''The number of minibatches to be ran before gradients are accumulated.'''			,       )
			parser.add_argument('''--cpu'''			,       action='''store_true'''			,       help='''If passed, will train on the CPU.'''		)
			UpperCAmelCase_      :       Union[str, Any]          =       parser.parse_args()
			UpperCAmelCase_      :       List[str]          =       {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(A			,       A		)


if __name__ == "__main__":
						main()










'''simple docstring'''





def __UpperCAmelCase   (					A					:			list		)    ->   list:
			if len(A		) <= 1:
						return lst

			UpperCAmelCase_      :       List[str]          =       1

			while i < len(A		):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												UpperCAmelCase_      :       Dict          =       1

			return lst


if __name__ == "__main__":
						_UpperCamelCase						:						int  =					input('Enter numbers separated by a comma:\n').strip()
						_UpperCamelCase						:						Union[str, Any]  =					[int(item) for item in user_input.split(',')]
						print(gnome_sort(unsorted))


